# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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"""Learning rate utilities for vision tasks."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from typing import Any, Mapping, Optional

import numpy as np
import tensorflow as tf

BASE_LEARNING_RATE = 0.1


class WarmupDecaySchedule(tf.keras.optimizers.schedules.LearningRateSchedule):
    """A wrapper for LearningRateSchedule that includes warmup steps."""

    def __init__(
            self,
            lr_schedule: tf.keras.optimizers.schedules.LearningRateSchedule,
            warmup_steps: int,
            warmup_lr: Optional[float] = None):
        """Add warmup decay to a learning rate schedule.

    Args:
      lr_schedule: base learning rate scheduler
      warmup_steps: number of warmup steps
      warmup_lr: an optional field for the final warmup learning rate. This
        should be provided if the base `lr_schedule` does not contain this
        field.
    """
        super(WarmupDecaySchedule, self).__init__()
        self._lr_schedule = lr_schedule
        self._warmup_steps = warmup_steps
        self._warmup_lr = warmup_lr

    def __call__(self, step: int):
        lr = self._lr_schedule(step)
        if self._warmup_steps:
            if self._warmup_lr is not None:
                initial_learning_rate = tf.convert_to_tensor(
                    self._warmup_lr, name="initial_learning_rate")
            else:
                initial_learning_rate = tf.convert_to_tensor(
                    self._lr_schedule.initial_learning_rate,
                    name="initial_learning_rate")
            dtype = initial_learning_rate.dtype
            global_step_recomp = tf.cast(step, dtype)
            warmup_steps = tf.cast(self._warmup_steps, dtype)
            warmup_lr = initial_learning_rate * global_step_recomp / warmup_steps
            lr = tf.cond(global_step_recomp < warmup_steps, lambda: warmup_lr,
                         lambda: lr)
        return lr

    def get_config(self) -> Mapping[str, Any]:
        config = self._lr_schedule.get_config()
        config.update({
            "warmup_steps": self._warmup_steps,
            "warmup_lr": self._warmup_lr,
        })
        return config


class CosineDecayWithWarmup(tf.keras.optimizers.schedules.LearningRateSchedule
                            ):
    """Class to generate learning rate tensor."""

    def __init__(self, batch_size: int, total_steps: int, warmup_steps: int):
        """Creates the cosine learning rate tensor with linear warmup.

    Args:
      batch_size: The training batch size used in the experiment.
      total_steps: Total training steps.
      warmup_steps: Steps for the warm up period.
    """
        super(CosineDecayWithWarmup, self).__init__()
        base_lr_batch_size = 256
        self._total_steps = total_steps
        self._init_learning_rate = BASE_LEARNING_RATE * batch_size / base_lr_batch_size
        self._warmup_steps = warmup_steps

    def __call__(self, global_step: int):
        global_step = tf.cast(global_step, dtype=tf.float32)
        warmup_steps = self._warmup_steps
        init_lr = self._init_learning_rate
        total_steps = self._total_steps

        linear_warmup = global_step / warmup_steps * init_lr

        cosine_learning_rate = init_lr * (
            tf.cos(np.pi * (global_step - warmup_steps) /
                   (total_steps - warmup_steps)) + 1.0) / 2.0

        learning_rate = tf.where(global_step < warmup_steps, linear_warmup,
                                 cosine_learning_rate)
        return learning_rate

    def get_config(self):
        return {
            "total_steps": self._total_steps,
            "warmup_learning_rate": self._warmup_learning_rate,
            "warmup_steps": self._warmup_steps,
            "init_learning_rate": self._init_learning_rate,
        }
